code: def test_audio_resample() -> None:
      opts = get_config()
      num_clips = 2
      num_samples = 32
      original_fps = 32
      new_fps = 8
      num_channels = 2
  
      setattr(
          opts, "audio_augmentation.audio_resample.audio_fps", new_fps
      )
      resample = AudioResample(opts)
      samples = {
          "audio": torch.arange(num_clips * num_samples * num_channels)
          .float()
          .view(num_clips, num_samples, num_channels),
          "metadata": {"audio_fps": original_fps},
      }
  
      new_data = resample({"samples": samples})
      assert new_data["samples"]["audio"].shape == (
          num_clips,
          new_fps,
          num_channels,
      )
      assert new_data["samples"]["metadata"]["audio_fps"] == new_fps
  
file_path: ml-cvnets/tests/transforms/test_audio.py
imports: import copy
  import os
  import shutil
  import tempfile
  import pytest
  import scipy.io.wavfile
  import torch
  from data.transforms.audio import AudioResample, Gain, LambdaAudio, MFCCs, Noise, Roll, SetFixedLength, StandardizeChannels
  from tests.configs import get_config
label_desc: Use image node_id 5 for calling a global function with example usage: test_audio_resample() without return types